In this work we performed a comprehensive literature review and identified an absence of research utilising both sensor fusion of remotely sensed data and deep learning models for AGB prediction. To address these shortcomings, we propose to extend a previous developed traditional non-sequential regression model from Næsset et al. [1]} to a modelling strategy with two sequential regression models. The previous developed regression model [1]} predicts AGB by relating ALS data to ground reference data of AGB. In order to enable prediction of AGB on a larger scale and at a low cost, the proposed additional regression model relates SAR backscatter from Sentinel-1A to these ALS-based AGB predictions using a deep learning architecture for cross-modal image-to-image translation. The low cost of our proposed, extended regression model is due to Sentinel-1A data being freely available. The cGAN architecture allows us to generate synthesised ALS-based AGB predictions during model fitting by conditioning our model on the ALS-based AGB predictions achieved from the first of the two sequential regression models. We therefore base our additional regression model on cGAN and the Pix2Pix architecture [3]} with some modifications warranted by the application.
